1273993 results (page 103 of 50960)
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2nd of the 5th PVUW MeViS-Audio Track: ASR-SaSaSa2VA
Audio-based video object segmentation aims to locate and segment objects in videos conditioned on audio cues, requiring precise understanding of both appearance and motion. Recent audio-driven video segmentation methods extend MLLMs by fusing audio and visual features for end-to-end localization. Despite their promise, these approaches are computationally intensive, struggle with aligning temporal…
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VLM-VPI: A Vision-Language Reasoning Framework for Improving Automated Vehicle-Pedestrian Interactions
Autonomous driving systems often infer pedestrian yielding behavior from geometric and kinematic cues alone, limiting their ability to reason about visual scene context and age-dependent behavioral variability. This limitation can produce delayed interventions in safety-critical encounters and unnecessary braking in benign interactions. This work introduces Vision-Language Model-based Vehicle-Pede…
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Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
Developing robust and clinically reliable EEG biomarkers requires evaluation frameworks that explicitly address cross population generalization in multi site settings such as Parkinsons disease (PD) detection. Models trained under i.i.d. assumptions often capture population specific artifacts rather than disease relevant neural structure, leading to poor generalization across clinical cohorts. EEG…
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Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data. We present a systematic empirical comparison of four VQC families -- multi-layer fully-connected (FC-VQC), residual (ResNet-VQC), hybrid quantum-classical transformer (Q…
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Nearly Optimal Subdata Selection
When, in terms of the number of data points, the size of a dataset exceeds available computing resources, or when labeling is expensive, an attractive solution consists of selecting only some of the data points (subdata) for further consideration. A central question for selecting subdata of size $n$ from $N$ available data points is which $n$ points to select. While an answer to this question depe…
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Uncovering the Rapidly Evolving Orbits of the Dynamic TOI-201 System
Studying planetary interactions in exoplanet systems informs theories of planet formation and evolution, providing essential context for understanding our own solar system. We combine spectroscopy, transit photometry, transit timing variations, and astrometry to characterize the TOI-201 system. The co-transiting system consists of a super-Earth, warm Jupiter, and massive companion at 5.8, 53, and …
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Towards Localizing Conversation Partners using Head Motion
Many individuals struggle to understand conversation partners in noisy settings, particularly amid background speakers or due to hearing impairments. Emerging wearables like smartglasses offer a transformative opportunity to enhance speech from conversation partners. Crucial to this is identifying the direction in which the user wants to listen, which we refer to as the user's acoustic zones of in…
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A very eccentric brown dwarf coplanar to a warm Jupiter and a hot super Earth
In transiting planetary systems, where planetary sizes are accurately determined from transit observations, the presence of transit timing variations (TTVs), especially when combined with radial velocity (RV) data, provides powerful constraints on masses and orbital eccentricities. Together, these measurements offer crucial insights into system architecture, formation mechanisms, and dynamical evo…
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Medium-Induced Cross-Frequency Clutter Structure in Single-Snapshot FDA-MIMO-GPR With a Weak-Dispersion Criterion
This paper investigates the cross-frequency structure of background clutter induced by random dispersive media in single-snapshot FDA-MIMO-GPR. Representative media are modeled by the Cole--Cole formulation to relate dispersive constitutive behavior to the reference propagation environment and observation-domain statistics. A normalized incremental contrast function is introduced under a reference…
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Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions
We instruct an AI agent to construct two separate agentic AI platforms: one for autonomous training of predictive ML models for human-human and virus-human PPI, and the other for inducing explicit general rules governing human-human and virus-human PPI. The first agentic AI platform for autonomous training of predictive ML models for PPI is designed to consist of five AI agents that handle autonom…
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Opto-Atomic Spatio-Temporal Holographic Correlators for High-Speed 3D CNNs
Three-dimensional convolutional neural networks (3D CNNs) have demonstrated remarkable performance in video recognition tasks by processing both spatial and temporal features. However, the cubic scaling of computational complexity poses significant time and energy efficiency challenges for conventional silicon-based hardware. To address this, we propose a hybrid optoelectronic architecture that de…
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Quasi-Quadratic Gradient: A New Direction for Accelerating the BFGS Method in Quasi-Newton Optimization
In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newton framework. By defining the QQG as the product of the inverse Hessian approximation and the current gradient, we explicitly leverage local second-order curvature to rectify the search path. Theoretical analysis and empirical results demonstrate that…
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Crystal structure prediction using graph neural combinatorial optimization
Crystalline materials are widely used in technological applications, yet their discovery remains a significant challenge. As their properties are driven by structure, crystal structure prediction (CSP) methods play a central role in computational approaches aiming to accelerate this process. Previously, CSP has been approached from a combinatorial optimization perspective, with the core challenge …
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MR-CCC: Bayesian Mendelian Randomization for Causal Cell--Cell Communication
Cell--cell communication (CCC) is commonly inferred from ligand--receptor co-expression, an associational paradigm that cannot distinguish causal signaling from shared regulation or confounding. We propose MR-CCC, a Bayesian Mendelian randomization framework that uses cis-eQTLs as instruments for ligand and receptor expression and explicitly models receptor-modulated ligand effects through an inte…
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GRRMHD Simulations of State Transitions in Non-Jetted Tidal Disruption Events
Circularization of the stream material into a debris cloud during tidal disruption events (TDEs) was recently demonstrated in one of the most accurate long duration TDE simulations to-date. The cooling envelope model (CEM) provides a description of the circularized debris cloud and its emission over time well beyond circularization across different disruption parameters. In the CEM, sub-Eddington …
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Improved global stability bounds for two-dimensional plane Poiseuille flow
This work provides new lower bounds on the global (nonlinear) stability limit of pressure-driven two-dimensional plane Poiseuille flow, improving on the energy stability limit, $Re_E$, originally computed by Orr in 1907. Using a computer we carefully construct quartic Lyapunov functionals of the velocity perturbations about the laminar profile, yielding rigorous nonlinear stability certificates. T…
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#MakeBeefGreatAgain: A Cross-Platform Analysis of Early #MAHA Discourse
Make America Healthy Again (MAHA) is a health-related campaign slogan proposed by Robert F. Kennedy Jr. and later incorporated into the political coalition of President Trump. While #MAHA quickly circulated beyond the campaign itself and became a prominent hashtag for public discussion, it remains unclear whether this public discourse reflected, reshaped, or diverged from the stated agenda of the …
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Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-based method that operates directly on distance matrices to learn a consensus embedding preserving shared relational structure. By leveraging intrinsic distances, the approach naturally handles nonlinear distortions acro…
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With arms wide open: a VLT/MUSE view of the mechanisms driving unwinding spiral arms in cluster galaxies
The environmental mechanisms driving unwinding spiral arms in cluster galaxies remain debated. While earlier studies attributed it mainly to gravitational interactions, recent works suggest that RPS alone can induce unwinding. We present a VLT/MUSE spatially resolved analysis to investigate the mechanisms responsible for spiral-arm unwinding in two galaxies, UG101 and UG103, drawn from a larger sa…
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AMAVA: Adaptive Motion-Aware Video-to-Audio Framework for Visually-Impaired Assistance
Navigational aids for blind and low vision individuals struggle conveying dynamic real-world environments, leading to cognitive overload from continuous, undifferentiated feedback. We present AMAVA, a novel real-time video-to-audio framework that converts mobile device video into contextually relevant sound effects or text-to-speech descriptions. We propose a motion-aware pipeline using a lightwei…
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Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non-stationarity. This phenomenon is pronounced in the South Australian region of the National Electricity Market, where high renewable penetration drives price volatility and frequent negative price intervals, while structural changes such as the…
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SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems
Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hybrid neuro-symbolic pipeline that starts from a SysML architecture model and produces a prioritized list of NIST 800-53 security controls. The prototype has three main stages: a deterministic parser mapping system components to vulnerabilities via th…
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Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
Synthetic data offers a promising tool for privacy-preserving data release, augmentation, and simulation, but its use in causal inference requires preserving more than predictive fidelity. We show that fully generative tabular synthesizers, including GAN- and LLM-based models, can achieve strong train-on-synthetic-test-on-real performance while substantially distorting causal estimands such as the…
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Integrative neurocybernetic modeling in the era of large-scale neuroscience
Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body and environment, treat the brain as a controller pursu…
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LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support
Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to adapt to dynamic traffic demand and provide limited decision interpretability. This study proposes an LLM-augmented traffic signal control framework that integrates LSTM-based short-term traffic state prediction, predictive phase selection, structure…